CN113156931B - Method for configuring path for mobile robot containing ion-artificial bee colony algorithm - Google Patents
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Abstract
The invention discloses a method for configuring a path for a mobile robot containing an ion-artificial bee colony algorithm, which is mainly used for solving the path planning problem in a storage environment; aiming at the defects of the traditional ABC algorithm, interaction force among ions in nature is introduced to improve the search stage of the bee colony algorithm, the search stage is divided into a front stage and a rear stage to balance the development and exploration capacity of the algorithm, the search step length is increased on the premise of ensuring that the algorithm is not involved in local optimization, adaptive flower fragrance concentration is added in the global update stage, variation adaptive update is guided according to the flower fragrance concentration, and the efficiency of the algorithm is improved. The algorithm verifies the solving capability of the extreme value under different standard test functions and shows great advantages, and the practical application effect of the algorithm is verified by solving the path planning problem of the robot.
Description
Technical Field
The invention relates to the application of the robot field, in particular to a method for configuring a path for a mobile robot with an ion-artificial bee colony algorithm.
Background
The path planning is one of core technologies of wheeled mobile robot navigation, and means that after a starting point and a target point of a robot are given in an environment with an obstacle, a safe and efficient motion path is provided for the robot according to a specific evaluation standard, and the evaluation standard generally comprises: the method is characterized by comprising the following steps of (1) shortest travel, shortest time, least energy and the like, and the traditional path planning method comprises an artificial potential field method, a graph search method, a grid decoupling method and the like; in recent years, as scholars at home and abroad make a great deal of research on the path planning method, a plurality of excellent group intelligent algorithms are provided: such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), firework-Ant Colony fusion Algorithm (FA-ACA), etc., these methods greatly improve the path planning performance, but have certain limitations, such as trapping in local Optimization, long operation time, complicated solution, etc.
An Artificial Bee Colony (ABC) algorithm is a Colony intelligent evolution algorithm proposed by Karaboga inspired by Bee foraging, the algorithm simulates mutual cooperative conversion of Bee honey collection to guide searching, and a standard ABC algorithm has the advantages of high convergence rate, strong optimizing capability, simplicity in implementation and the like, but also has the defects of local optimization, poor balancing capability, relatively low precision and the like caused by excessively high later convergence rate.
Disclosure of Invention
The invention aims to solve the technical problems that when the robot uses the defects of poor balance capability, relatively low precision, low convergence speed and the like of an artificial bee colony algorithm, and aims to provide a method for configuring a path for a mobile robot containing an ion-artificial bee colony algorithm, and the development and exploration capability of the bee colony algorithm can be more effectively balanced in path planning.
The invention is realized by the following technical scheme:
a method of configuring a path for a mobile robot including an ion-artificial bee colony algorithm, comprising the steps of:
s1: configuring a target motion map parameter, a starting point coordinate function parameter and a target point coordinate function parameter in a processing system of the mobile robot;
s2: an ion-artificial bee colony calculation module in the processing system obtains the optimal solution of the mobile robot by processing the parameters of the configured target motion map, the initial point coordinate function parameters and the target point coordinate function parameters;
s3: the processing system of the mobile robot regards the optimal solution as the shortest motion path of the mobile robot;
s4: and the mobile robot executes the motion instruction according to the shortest motion path obtained by the processing system.
Further, S21: initializing relevant parameters of an ion-artificial bee colony algorithm preset by a processing system to obtain initialized relevant parameters;
s22: inputting the initialized relevant parameters into a feasible space, and starting to obtain an initial population by the initialized parameters in the feasible space, wherein the iteration number iter is set to be 0;
s23: for the generated initialization population, generating new solutions of leading bees and following bees by adopting an ion motion law algorithm, and then reserving an optimal solution;
s24: the processing system program detects whether iter is less than N/2, if iter is less than N/2, then proceed to the next step S25, if iter is greater than or equal to N/2; returning to step S23 for repeated iteration;
s25: after the iteration number iter is smaller than N/2, the leading bees and the following bees start to generate new individuals, and better solutions of the leading bees and the following bees after the new individuals are generated are reserved;
s26: after obtaining a better solution of the leading bees and the following bees after generating new individuals, the system detects whether the i position Limit is updated, if not, the detecting bees abandon the i position Limit, the detecting bees select the individuals according to the adaptive floral scent factors and update the population, and the step S3 is returned; if the i-position Limit has been updated, the next step S27 is performed;
s27: the processing system judges whether iter is greater than or equal to N; if iter is less than N, returning to step S24, if iter is greater than or equal to N, proceeding to the next step S28;
s28: the processing system outputs the optimal solution of the mobile robot after judging the maximum iteration times or the target precision reached by the iter;
s29: the processing system of the mobile robot regards the optimal solution as the shortest motion path of the mobile robot.
Further, the related parameters of the ion-artificial bee colony algorithm are a target motion map parameter, a starting point coordinate function parameter, a target point coordinate function parameter population quantity SUM, a maximum iteration number N and a control parameter Limit.
Further, the new solution is generated by utilizing the ion motion law, and the leading bees and the following bees are expressed as follows:
A i,j ＝A i,j +AF i,j *(B bestj -A j ) And B i,j ＝B i,j +BF i,j *(A bestj -B j ) Generating a new solution A i,j 、B i,j 。
Further, a new solution A is generated i,j 、B i,j Push-press type
And calculating and reserving a better solution.
Further, the leading bee is pressed
Randomly generating a new solution v i,j In parallel pressing mode
The better solution is retained.
Further, the follower bee follows the formula
The mechanism of reverse roulette selects individuals according to the formula
Is/are as follows
Generation of New individuals v i,j According to formula
The better solution is retained.
Furthermore, the robot containing the ion-artificial bee colony algorithm path planning can more effectively balance the development and exploration capabilities of the bee colony algorithm in the path planning, the evolution direction can be adjusted according to the advantages and disadvantages of the surrounding individual fitness values in order to balance the development capabilities and the exploration capabilities of the algorithm, and then the ion cross search rule is introduced, so that the population overall evolution speed and effect can be improved.
Further, a target motion map parameter, an initial point coordinate function parameter, a target point coordinate function parameter, a population quantity SUM, a maximum iteration number N and a control parameter Limit are arranged in the mobile robot; the robot is provided with a target motion map parameter, an initial point coordinate function parameter, a target point coordinate function parameter, a population quantity SUM, a maximum iteration number N and a window of a control parameter Limit, and the mobile robot is provided with an ion-artificial bee colony calculation module and a system processing module.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the invention relates to a method for configuring a path for a mobile robot containing an ion-artificial bee colony algorithm, which can more effectively balance the development and exploration capabilities of the bee colony algorithm in path planning, can adjust the evolution direction according to the advantages and disadvantages of the fitness values of surrounding individuals in order to balance the development capabilities and the exploration capabilities of the algorithm, and then introduces an ion cross search rule, thereby being beneficial to improving the overall evolution speed and effect of a population. Compared with the traditional bee colony and other colony intelligent algorithms, the improved algorithm greatly improves the convergence speed and precision of the algorithm on the premise of avoiding local optimization.
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The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a flow chart of the IM-ABC algorithm of the present invention.
FIG. 2 is a schematic diagram of an ABC algorithm robot of the present invention.
FIG. 3 is a schematic diagram of the IM-ABC algorithm robot of the present invention.
FIG. 4 is a schematic diagram of an ABC algorithm robot MATLAB.
FIG. 5 is a schematic diagram of an IM-ABC algorithm robot MATLAB.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that: it is not necessary to employ these specific details to practice the present invention. In other instances, well-known structures, circuits, materials, or methods have not been described in detail so as not to obscure the present invention.
Throughout the specification, reference to "one embodiment," "an embodiment," "one example," or "an example" means: the particular features, structures, or characteristics described in connection with the embodiment or example are included in at least one embodiment of the invention. Thus, the appearances of the phrases "one embodiment," "an embodiment," "one example" or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combination and/or sub-combination in one or more embodiments or examples. Further, those of ordinary skill in the art will appreciate that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
In the description of the present invention, it is to be understood that the terms "front", "rear", "left", "right", "upper", "lower", "vertical", "horizontal", "high", "low", "inner", "outer", etc. indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and therefore, are not to be construed as limiting the scope of the present invention.
Examples
As shown in fig. 1, the method for configuring a path for a mobile robot including an ion-artificial bee colony algorithm of the present invention adopts different strategies before and after a search stage in order to avoid the disadvantages of poor balance capability, easy falling into local optimum, and the like of the conventional artificial bee colony algorithm. An ion cross search rule is provided at the early stage of the algorithm, so that the population development capability is improved; in the later period, a reverse roulette is adopted to enlarge the diversity of the population and avoid falling into local optimum; in the global updating stage, the self-adaptive flower fragrance concentration rule is provided, the sampling mode is improved, and the population updating direction is guided. Compared with other group intelligent algorithms, the algorithm can balance the development and exploration capacity of the algorithm more effectively in path planning, and greatly improves the convergence speed and precision of the algorithm on the premise of avoiding local optimization.
The ABC algorithm is proposed by simulating the honey collection process of bees, wherein bee colonies are mainly divided into three categories: leading bee, follower bee and scout bee [15] . The three types of bees cooperate and share information in the honey collection process, the guidance bee information is obtained by the following bees and then evaluated, the proper flower source is selected according to the quality for honey collection, the reconnaissance bees update the poor honey source when the honey source is exhausted, and each honey source represents a feasible solution of path planning. The ABC algorithm optimizing steps are as follows:
1) and (5) an initialization phase. Firstly, the SUM number, the maximum iteration number N and the control parameter Limit of the swarm are set, and M initial positions X are randomly generated in an S-dimensional space i ＝(x i1 ，x i2 ，...，x iS )，X i The generation is performed according to equation (1):
x i，j ＝W i，j +rand(0，1)(U i，j -w i，j ) (1)
wherein: w i,j And U i,j Upper and lower bounds for the S-dimensional space value, i 1,2, …, M, j 1,2, …, S; m represents the number of solutions, and generally takes the value SUM/2.
2) And (5) a searching stage. The half with smaller fitness value in the population is the leading bee, and the other half isFollowing bees, randomly selecting individuals by following bees
The search is performed dimension by dimension to generate a new individual V, as in equation (3).
In the formula:
is [1, -1 ]]A random number in between.
Selecting probability p in leading bee colony by following bee according to roulette i Larger individuals, then in [0,1 ]]Internally generating a random number if p i Greater than the generated random number, position J is selected according to equation (4):
and in the formula (4), fit is a honey source fitness value, and updating is carried out according to the formula (5). And after the leading bees and the following bees generate new solutions, greedy selection is carried out according to the formula (6) and the new solutions are reserved.
3) And a global updating phase. After the search of leading bee and following bee populations is completed, in order to avoid loss of population diversity, if the position i is continuously unchanged by the Limit generation, a substitute solution is randomly generated according to the formula (1), then the search process of hiring bees and following bees is returned, and the process is repeatedly cycled until an optimal solution is found.
In the standard ABC algorithm, the process of learning from the lead bee to other individuals can be actually understood by searching for the lead bee, the update of the lead bee in the algorithm is to randomly select the individuals around the position of the lead bee to perform cross update, so that the excellent probability and the poor probability of the selected individuals are equal, although the population diversity is expanded to a certain extent, the convergence rate of the algorithm is greatly reduced, namely the method has strong exploration capability and poor development capability [16] . In order to balance the development capability and the exploration capability of the algorithm, the evolution direction can be adjusted according to the advantages and disadvantages of the fitness values of the surrounding individuals, so that the evolution speed and the evolution effect of the population are improved.
Considering that the follow bee updates the population according to the roulette selection strategy is a relatively greedy mode, the population diversity can be rapidly reduced in the whole process of the algorithm, premature convergence and local optimization are easily caused, and in order to avoid local optimization on the premise of ensuring the convergence speed, a self-adaptive factor needs to be introduced for adjustment. In order to obtain better effect of the algorithm, the searching stage of the algorithm can be divided into two stages.
In nature, ions of similar charge tend to repel, while ions of opposite charge attract each other. The relationship between the attractive and repulsive forces and the anions and cations is shown in fig. 1, with the solid arrows representing the attractive force and the dashed arrows representing the approach of the ions to the best cations and the movement of the cations to the best anions under the attractive/repulsive force.
In order to balance the development and exploration capacity of the algorithm, the algorithm searching stage is divided into a front stage and a rear stage, and an ion cross searching rule is introduced:
1) and (3) introducing an ion motion cross search equation in the early stage of the search stage in the ABC algorithm, and if the leading bees are positive ions and the following bees are negative ions, and information transmitted between the bees is compared with the inter-ion attraction, so that the leading bees and the following bees are alternately updated as shown in formulas (7) and (8). Selecting individuals with optimal fitness value for cross learning by leading bees and following bees, and introducing gravity factor AF i,j 、BF i,j The method can adaptively accelerate algorithm evolution speed and promote population rapid convergence.
A i,j ＝A i,j +AF i,j *(B bestj -A j ) (7)
B i,j ＝B i,j +BF i,j *(A bestj -B j ) (8)
In the formula: a. the i,j 、B i,j Respectively leading bees and follower bees; a. the bestj 、B bestj Representing the optimal leading bees and following bees in the population.
2) In the later stage of the search stage, population individuals are mostly in a better state, the evolution information of the excellent individuals does not play a leading role any more, the development capacity in the earlier stage needs to be balanced, the local exploration capacity of the bee colony is increased, the bee colony can be finely searched nearby the self field, the larger search step length as in the earlier stage is not needed, the local optimum caused by the larger search step length is avoided, and the probability of finding the globally optimum solution is reduced.
In the later period, leading bees search according to the formula (3), and the generated new solution is selectively replaced according to the formula (6). A reverse roulette selection mechanism is introduced by following bees according to the formula (11), the probability that an individual with a high inverse fitness value is selected is high, and local optimization can be effectively skipped.
In the global updating stage, because the reconnaissance bee position updating mode has high randomness and belongs to the replacement type sampling, the poor honey source can be subjected to useless updating calculation for many times, the practical significance is not proper, a self-adaptive flower fragrance concentration information strategy can be added, the flower fragrance concentration added with the self-adaptive factor is updated according to the flower fragrance concentration, the self-adaptive factor can adjust the flower fragrance concentration in each cycle, when the reconnaissance bee finds that the honey source in a certain direction is poor, the direction cannot be updated for the second time, and the reconnaissance bee calculates the position updating probability according to the formula (12).
In the formula: KT (karat) i Is the concentration of the floral aroma per dimension,
is a self-adaptive parameter, Max _ Cycle is the maximum iteration number of the algorithm, the concentrations are equal initially, and the dimension KT after elimination i And zero is set, so that secondary updating in the same direction is avoided, meanwhile, the directivity is provided for scout bee updating in a self-adaptive manner, the algorithm convergence is accelerated to a certain extent, and local optimization cannot be caused.
The IM-ABC algorithm flow is as follows:
step 1 sets initialization related parameters, including the number of clusters SUM, the maximum iteration number N, and the control parameter Limit.
Step2 generates an initial population in a feasible space, and sets the iteration number iter equal to 0.
Step3 uses the ion motion law to generate a new solution, and the leading bee and the following bee generate a new solution A according to the formulas (7) and (8) i,j 、B i,j 。
New solution A generated by Step 4 i,j 、B i,j The better solution is retained according to equation (6).
Step5 jumps back to Step2 and Step3 for repeated iteration, and if the iteration number iter is less than N/2, then Step 6 is jumped to.
Step 7 follows the bee to select individuals according to the mechanism of the reverse roulette of equation (11) and generate new individuals v according to equation (3) i,j And a better solution is reserved according to the formula (6).
And Step 9, judging whether the maximum iteration number or the target precision is reached, if so, outputting the optimal solution, and otherwise, skipping to Step 5.
To verify the effectiveness of the IM-ABC algorithm, four standard test functions commonly used by researchers were chosen here: the method comprises the following steps of testing and comparing a traditional ABC algorithm with a Fast Search Artificial Bee Colony (FSABC) algorithm and a Local Route Artificial Bee Colony (LRABC) algorithm which have excellent performance at present through a Sphere function, a Rosenbrock function, a Rastrigin function and a Griwank function, wherein the FSABC algorithm adopts a Local Search operator to remarkably improve solving precision [17] The convergence rate of the LRABC algorithm is greatly improved [18] However, both algorithms still have certain disadvantages in terms of development capability.
In the experiment, parameters of four algorithms are set, the total group number SUM in the algorithm is set to be 100, the number of leading bees and the number of following bees are respectively set to be 50, the Limit value is set to be 750, the dimensionality of a test function is 30, the maximum iteration number N is set to be 2000, and the performance of the algorithm is measured by the optimal value, the worst value, the average value and the variance of experimental data. The test function information is shown in table 1, the test result data is shown in table 2, and the experimental result shows that the IM-ABC algorithm provided by the invention has better precision than the traditional ABC algorithm and the other two ABC improved algorithms and has good searching capability.
TABLE 1 Standard test function
TABLE 2 Algorithm simulation comparison results
The method is applied to solving the robot path planning problem, and in order to verify the actual application effect of the algorithm, the complex and changeable storage environment in reality is simulated by establishing a grid model. As shown in fig. 2, taking a grid environment of 20m × 20m as an example, in the grid map, the working environment is divided into two grids, a black grid represents an obstacle, a white grid represents no obstacle, and the grids are respectively represented by 1 and 0 in the system simulation program, and the size and dimension of the grid map can be adjusted according to the actual environment.
Experiments are simulated by Matlab 2016a software, and the shortest path from a starting point A to the center of a target point B is searched on a grid map by testing an ABC algorithm and an IM-ABC algorithm so as to simulate the shortest path in an actual environment. In a grid map environment of a size of 20m × 20m, the swarm size SUM is set to 100, the maximum number of iterations N is 200, and the Limit value is set to 10.
The ion artificial bee colony algorithm is applied to robot path planning, and the optimizing performance and the convergence speed of the robot are effectively improved, as shown in fig. 2 and 3.
As shown in fig. 3 and 4, the simulation iteration number of the algorithm is reduced by 58.3% when the algorithm is applied to the robot, the optimization performance is improved by 12.6%, and the high planning efficiency is shown.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only examples of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (6)
1. A method for configuring a path for a mobile robot including an ion-artificial bee colony algorithm, comprising the steps of:
s1: configuring a target motion map parameter, a starting point coordinate function parameter and a target point coordinate function parameter in a processing system of the mobile robot;
s2: an ion-artificial bee colony calculation module in the processing system obtains the optimal solution of the mobile robot by processing the parameters of the configured target motion map, the initial point coordinate function parameters and the target point coordinate function parameters;
the specific method for obtaining the optimal solution of the mobile robot by the ion-artificial bee colony calculation module is as follows:
s21: initializing relevant parameters of an ion-artificial bee colony algorithm preset by a processing system to obtain initialized relevant parameters;
s22: inputting the initialized relevant parameters into a feasible space, and starting to obtain an initial population by the initialized parameters in the feasible space, wherein the iteration number iter is set to be 0;
s23: for the generated initialization population, generating new solutions of leading bees and following bees by adopting an ion motion law algorithm, and then reserving an optimal solution;
the new solution is generated by utilizing the ion motion law, and the bee leading and the bee following are expressed as follows:
A i,j ＝A i,j +AF i,j *(B bestj -A j ) And B i,j ＝B i,j +BF i,j *(A bestj -B j ) To generate a new solution A i,j 、B i,j (ii) a Wherein, AF i,j 、BF i,j Denotes the gravitational factor, A i,j 、B i,j Respectively representing the newborn individuals generated by leading bees and following bees; a. the bestj 、B bestj Representing the optimal leading bees and follower bees in the population;
s24: the processing system program detects whether iter is less than N/2, if iter is less than N/2, then proceed to the next step S25, if iter is greater than or equal to N/2; returning to step S23 for repeated iteration;
s25: after the iteration number iter is smaller than N/2, the leading bees and the following bees start to generate new individuals, and better solutions of the leading bees and the following bees after the new individuals are generated are reserved;
s26: after obtaining a better solution of the leading bees and the following bees after generating new individuals, the system detects whether the i position Limit is updated, if not, the detecting bees abandon the i position Limit, the detecting bees select the individuals according to the adaptive floral scent factors and update the population, and the step S23 is returned; if the i-position Limit has been updated, the next step S27 is performed;
the process of updating the population after the detection bees select individuals according to the self-adaptive floral scent factors is as follows:
the scout bee calculates the position update probability PT according to the following formula i ：
Wherein, KT i The floral concentration is expressed in terms of each dimension,
representing a self-adaptive parameter, and representing the maximum iteration number of the algorithm by Max _ Cycle;
s27: the processing system judges whether iter is greater than or equal to N; if iter is less than N, returning to step S24, if iter is greater than or equal to N, proceeding to the next step S28;
s28: the processing system outputs the optimal solution of the mobile robot after judging the maximum iteration times or the target precision reached by the iter;
s3: the processing system of the mobile robot regards the optimal solution as the shortest motion path of the mobile robot;
s4: and the mobile robot executes the motion instruction according to the shortest motion path obtained by the processing system.
2. The method of configuring a path for a mobile robot including an ion-artificial bee colony algorithm according to claim 1, wherein the relevant parameters of the ion-artificial bee colony algorithm are a target motion map parameter, a start point coordinate function parameter, a target point coordinate function parameter population number SUM, a maximum iteration number N, and a control parameter Limit.
3. The method for configuring path for mobile robot including ion-artificial bee colony algorithm according to claim 1, wherein the generated new solution A i,j 、B i,j Push-press type
And calculating and reserving a better solution.
4. The method for configuring path for mobile robot containing ion-artificial bee colony algorithm according to claim 1, wherein the leading bee is pressed
Randomly generating a new solution v i,j Parallel pressing type
The better solution is retained.
5. The method of configuring a path for a mobile robot including an ion-artificial bee colony algorithm as claimed in claim 1, wherein the follower bees are in accordance with
The mechanism of reverse roulette selects individuals according to the formula
Generating a New Individual v i,j According to the formula
The better solution is retained.
6. The method for configuring a path for a mobile robot including an ion-artificial bee colony algorithm according to claim 1, wherein the mobile robot has built therein a target motion map parameter, a start point coordinate function parameter, a target point coordinate function parameter, a population number SUM, a maximum iteration number N, and a control parameter Limit; the robot is provided with a target motion map parameter, an initial point coordinate function parameter, a target point coordinate function parameter, a population quantity SUM, a maximum iteration number N and a window of a control parameter Limit, and the mobile robot is provided with an ion-artificial bee colony calculation module and a system processing module.
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